Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ags/aaea09/49117.html
   My bibliography  Save this paper

Extreme coefficients in Geographically Weighted Regression and their effects on mapping

Author

Listed:
  • Cho, Seong-Hoon
  • Lambert, Dayton M.
  • Kim, Seung Gyu
  • Jung, Suhyun
Abstract
This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.

Suggested Citation

  • Cho, Seong-Hoon & Lambert, Dayton M. & Kim, Seung Gyu & Jung, Suhyun, 2009. "Extreme coefficients in Geographically Weighted Regression and their effects on mapping," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49117, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea09:49117
    DOI: 10.22004/ag.econ.49117
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/49117/files/Selected_paper_613303_Cho_et_al.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.49117?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Deller, Steven C. & Lledo, Victor, 2007. "Amenities and Rural Appalachia Economic Growth," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 36(1), pages 1-26, April.
    2. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    3. Lambert, Dayton M. & McNamara, Kevin T. & Garrett, Megan I., 2006. "An Application of Spatial Poisson Models to Manufacturing Investment Location Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(1), pages 1-17, April.
    4. Dan-Lin Yu, 2006. "Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 40(1), pages 173-190, March.
    5. Yefang Huang & Yee Leung, 2002. "Analysing regional industrialisation in Jiangsu province using geographically weighted regression," Journal of Geographical Systems, Springer, vol. 4(2), pages 233-249, June.
    6. Cho, Seong-Hoon & Bowker, James Michael & Park, William M., 2006. "Measuring the Contribution of Water and Green Space Amenities to Housing Values: An Application and Comparison of Spatially Weighted Hedonic Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(3), pages 1-23, December.
    7. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
    8. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis," Environment and Planning A, , vol. 30(11), pages 1905-1927, November.
    9. Partridge, Mark D. & Rickman, Dan S., 2007. "Persistent Pockets of Extreme American Poverty and Job Growth: Is There a Place-Based Policy Role?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 32(1), pages 1-24, April.
    10. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    11. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    12. Seong-Hoon Cho & Christopher D. Clark & William M. Park & Seung Gyu Kim, 2009. "Spatial and Temporal Variation in the Housing Market Values of Lot Size and Open Space," Land Economics, University of Wisconsin Press, vol. 85(1), pages 51-73.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabián Santos & Valerie Graw & Santiago Bonilla, 2019. "A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-37, December.
    2. Hans Koster & Jos van Ommeren & Piet Rietveld, 2011. "Geographic Concentration of Business Services Firms: A Poisson Sorting Model," ERSA conference papers ersa11p750, European Regional Science Association.
    3. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    4. Paliska, Dejan & Drobne, Samo, 2020. "Impact of new motorway on housing prices in rural North-East Slovenia," Journal of Transport Geography, Elsevier, vol. 88(C).
    5. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    6. López-Carr, David & Davis, Jason & Jankowska, Marta M. & Grant, Laura & López-Carr, Anna Carla & Clark, Matthew, 2012. "Space versus place in complex human–natural systems: Spatial and multi-level models of tropical land use and cover change (LUCC) in Guatemala," Ecological Modelling, Elsevier, vol. 229(C), pages 64-75.
    7. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    8. Yujiao Chen & Zhengbo Luo, 2022. "Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    9. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312.
    10. Koster, Hans R.A. & van Ommeren, Jos & Rietveld, Piet, 2014. "Estimation of semiparametric sorting models: Explaining geographical concentration of business services," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 14-28.
    11. Feuillet, T. & Commenges, H. & Menai, M. & Salze, P. & Perchoux, C. & Reuillon, R. & Kesse-Guyot, E. & Enaux, C. & Nazare, J.-A. & Hercberg, S. & Simon, C. & Charreire, H. & Oppert, J.M., 2018. "A massive geographically weighted regression model of walking-environment relationships," Journal of Transport Geography, Elsevier, vol. 68(C), pages 118-129.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sven Müller, 2012. "Identifying spatial nonstationarity in German regional firm start-up data," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 113-132, September.
    2. Anping Chen & Marlon Boarnet & Mark Partridge & Wenjie Wu & Guanpeng Dong, 2014. "Valuing The “Green” Amenities In A Spatial Context," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 569-585, September.
    3. Cem Ertur & Julie Le Gallo, 2008. "Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches," Working Papers hal-00463274, HAL.
    4. Wenjie Wu, 2012. "Spatial Variations in Amenity Values: New Evidence from Beijing, China," SERC Discussion Papers 0113, Centre for Economic Performance, LSE.
    5. Dziauddin, Mohd Faris, 2019. "Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR)," Research in Transportation Economics, Elsevier, vol. 74(C), pages 10-20.
    6. repec:rre:publsh:v:51:y:2021:i:2 is not listed on IDEAS
    7. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    8. Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
    9. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    10. Wrenn, Douglas H. & Sam, Abdoul G., 2014. "Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 60-74.
    11. Cai, Ruohong & Yu, Danlin & Oppenheimer, Michael, 2014. "Estimating the Spatially Varying Responses of Corn Yields toWeather Variations using GeographicallyWeighted Panel Regression," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(2), pages 1-23.
    12. Redfearn, Christian L., 2009. "How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets," Regional Science and Urban Economics, Elsevier, vol. 39(3), pages 297-306, May.
    13. Tsimpanos, Apostolos & Tsimbos, Cleon & Kalogirou, Stamatis, 2018. "Assessing spatial variation and heterogeneity of fertility in Greece at local authority level," MPRA Paper 100406, University Library of Munich, Germany.
    14. Diana Gutiérrez Posada & Fernando Rubiera Morollón & Ana Viñuela, 2018. "Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(3), pages 332-349, July.
    15. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    16. Malik, Khyati & Kim, Sowon & Cultice, Brian J., 2023. "The impact of remote work on green space values in regional housing markets," Journal of Housing Economics, Elsevier, vol. 62(C).
    17. Arnab Bhattacharjee & Eduardo Castro & Taps Maiti & João Marques, 2014. "Endogenous spatial structure and delineation of submarkets: A new framework with application to housing markets," SEEC Discussion Papers 1403, Spatial Economics and Econometrics Centre, Heriot Watt University.
    18. Aguilar, Francisco X., 2009. "Spatial econometric analysis of location drivers in a renewable resource-based industry: The U.S. South Lumber Industry," Forest Policy and Economics, Elsevier, vol. 11(3), pages 184-193, May.
    19. Christos Agiakloglou & Cleon Tsimbos & Apostolos Tsimpanos, 2019. "Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes," Empirical Economics, Springer, vol. 57(5), pages 1613-1631, November.
    20. Shaoming Cheng & Huaqun Li, 2010. "The effects of unemployment on new firm formation revisited: Does space matter?," Regional Science Policy & Practice, Wiley Blackwell, vol. 2(2), pages 97-120, November.
    21. Antonio Páez & Fei Long & Steven Farber, 2008. "Moving Window Approaches for Hedonic Price Estimation: An Empirical Comparison of Modelling Techniques," Urban Studies, Urban Studies Journal Limited, vol. 45(8), pages 1565-1581, July.

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea09:49117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.